D运输问题

提出一类要求货物尽量在某一给定时间以前如数运抵目的地的运输问题。这是一类含离散目标约束的目标规划问题，我们把它称为D运输问题。建立D运输问题的数学模型，引入可实施解、最优解、解对预警时间的偏差等概念，给出D运输问题的求解方法和一个计算例子。     

城市交通连续平衡网络设计问题的模拟退火算法

对城市交通连续平衡网络设计问题作一简要介绍。建立该问题的双层规划模型。针对该问题．构建一种特殊的邻城结构．并设计出基于此邻城结构的模拟退火算法。通过一个实例对算法进行验证．并同其他算法进行比较．结果表明模拟退火算法的计算结果较精确．但相应的计算量增加。但该算法用于大型网络设计问题时．其优势则会有明显体现。     

Relationships of Aegilops tauschii revealed by DNA fingerprints: The evidence for agriculture exchange between China and the West

Genetic diversity and relationships of wild goat grass (Aegilops tauschii Cosson) from Iran and Xinjiang,west China,as well as its weedy type from the Yellow River region of Shaanxi and Henan provinces in China were analyzed by simple sequence repeat (SSR) fingerprints.A high level of genetic diversity in Aegilops tauschii accessions from Iran was observed,and the richness of genetic diversity was followed by accessions from Shaanxi,Henan,and Xinjiang.The weedy type of Aegilops tauschii showed a close genetic relationship with the wild type from different regions in Iran.The results indicated that the weedy Aegilops tauschii found in the Yellow River region was most likely introduced from lran-the diversity center of Aegilops tauschii.The weedy Aegilops tauschii populations found in the Yellow River region may be brought into the central part of China as a weed species together with common wheat (Triticum aestivum L.) from the West during various periods of time in history.This finding has provided strong evidence for the introduction of common wheat from the West into China via the Silk Road,and also demonstrated the important role of the Silk Road in the exchange of agri-culture and other relevant technologies between China and the West.     

缺货补偿条件下的生产规划模型及算法

针对缺货补偿条件下有能力约束、多产品、多阶段制造系统的生产规划问题，以利润最大为目标，建立混合整数规划模型。通过对模型的等价转换，基于对偶理论将等价模型分解为相互关联的生产主问题和库存子问题，设计对偶分解算法进行求解。数值实例的计算结果，说明了算法的收敛趋势以及在计算时间方面的性能。     

基于多散射中心模型的雷达目标识别方法

针对宽带毫米波雷达目标识别要求 ,提出了一种基于目标光学区多散射中心模型进行目标匹配识别的新方法。该方法首先基于多散射中心理论建立待识别目标的模型、利用遗传算法的全局优化能力进行目标识别。与常规的全姿态角匹配识别方法比较 ,具有需要的存储量、计算量较少的特点。仿真试验验证了该算法的有效性。     

Genetic determinants of individual differences in avoidance learning: Behavioral and endocrine characteristics

Bidirectional genetic selection for good and poor active avoidance learning in a shuttle box has been carried out in three independent laboratories using remarkably similar discrete-trial training procedures. The resulting strains are known as the Roman High and Low Avoidance (RHA and RLA), the Syracuse High and Low Avoidance (SHA and SLA) and the Australian High and Low Avoidance (AHA and ALA) strains, respectively. An additional unidirectionally selected strain, known as the Tokai High Avoider (THA) strain was developed in Japan using a free-operant Sidman avoidance procedure in a Skinner box. This paper reviews the selection of the Syracuse strains, enumerates the various behavioral and endocrine characteristics of the strains, and compares them to the other similarly selected strains. The behavioral work suggests that genetic selection from diverse breeding stocks has resulted in common characteristics that differentiate the strains in the emotional, not learning, domain. The endocrine data, however, are somewhat at odds. The Syracuse strains differentiate one way with respect to endocrine function, and the Roman strains differentiate in the opposite way. We suggest, therefore, that the endocrine correlates are not tightly linked to the avoidance genotype. Genetic analysis of all of the selected strains for both the avoidance phenotype and the endocrine correlates will be needed to test this hypothesis.Preparation of this paper was supported by research grant MH-39230-3 from the National Institute of Mental Health.     

随机型网络瓶颈容量扩张的机会约束模型与智能算法

文章研究的问题为，在不确定环境中的机会约束下，怎样去增加一组边的容量到一个指定的瓶颈容量，以至于网络瓶颈扩张的费用最小。本文假定每一条边的单位扩张费用w1是一个随机变量，服从一定的概率分布。带有随机单位扩张费用W的网络瓶颈容量扩张问题可以根据一些概率机会约束规则，列出它的机会约束规划模型的通用表达式。随后，本文将网络瓶颈容量算法、随机模拟方法、神经网络和遗传算法合成在一起，设计出该问题的混合智能通用算法。最后，给出数值案例。     

无界报酬向量值折扣马氏决策规划

本文建立了一类无界向量值报酬折扣马氏决策规划,在一组无关向量生成的凸锥确定的序关系下,讨论了模型最优策略的存在性;给出强最优策略存在的必要充分条件;指出最优策略的自组合、凸组合策咯仍是最优策略;还证明了平稳策略在一般策略类中的优势。     

目标规划模型的知识表示与XPGP搜索算法研究

针对目标规划问题的建模与求解的问题，通过剖析目标规划数学模型的结构特征及其模型求解过程的状态转移规律，以及目标规划的求解原理与人工智能的状态空间理论分析对比，扩展人工智能状态空间三要素表示体系，提出了一种基于状态空间理论的目标规划模型的知识表示方法——Mpsp法；根据线性目标规划模型的结构和求解特点，提出了线性目标规划知识化模型改进的宽度优先搜索算法——XPGP搜索算法。本项研究使目标规划的应用研究朝着知识化、智能化方向迈进一步，充实了智能运筹学这一新学科的研究基础。     

一类随机决策模型的期望最优解及其经济意义

以线性多目标规划有效解的有效率及相关理论为基础，定义一类随机决策模型的期望最优解及相应的最优率，指出该随机决策模型的求解可只考虑相对应的线性多目标规划的非劣极点，因为只有这些非劣极点处的最优率才可能不为0。最后举例予以说明。